Once in a great while, as the New York Times noted recently, a naturalist writes a book that changes the way people look at the living world. John James Audubon’s Birds of America, published in 1838, was one. Roger Tory Peterson’s 1934 Field Guide to the Birds was another. How does such insight into nature develop? Pioneering a new niche in the study of plants and animals in their native habitat, Field Notes on Science and Nature allows readers to peer over the shoulders and into the notebooks of a dozen eminent field workers, to study firsthand their observational methods, materials, and fleeting impressions. What did George Schaller note when studying the lions of the Serengeti? What lists did Kenn Kaufman keep during his 1973 “big year”? How does Piotr Naskrecki use relational databases and electronic field notes? In what way is Bernd Heinrich’s approach “truly Thoreauvian,” in E. O. Wilson’s view? Recording observations in the field is an indispensable scientific skill, but researchers are not generally willing to share their personal records with others. Here, for the first time, are reproductions of actual pages from notebooks. And in essays abounding with fascinating anecdotes, the authors reflect on the contexts in which the notes were taken. Covering disciplines as diverse as ornithology, entomology, ecology, paleontology, anthropology, botany, and animal behavior, Field Notes offers specific examples that professional naturalists can emulate to fine-tune their own field methods, along with practical advice that amateur naturalists and students can use to document their adventures.

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Subdivision Methods for Geometric Design provides computer graphics students and designers with a comprehensive guide to subdivision methods, including the background information required to grasp underlying concepts, techniques for manipulating subdivision algorithms to achieve specific effects, and a wide array of digital resources on a dynamic companion Web site. Subdivision Methods promises to be a groundbreaking book, important for both advanced students and working professionals in the field of computer graphics.

Thoroughly revised and up-dated edition of a highly successful textbook.

Of interest to developers of virtual reality applications and others interested in potential uses for virtual reality, this book presents a selection of useful VR applications and gives readers guidance on how VR might be applied.

Location-based services (LBS) are a new concept integrating a user's geographic location with the general notion of services, such as dialing an emergency number from a cell phone or using a navigation system in a car. Incorporating both mobile communication and spatial data, these applications represent a novel challenge both conceptually and technically. The purpose of this book is to describe, in an accessible fashion, the various concepts underlying mobile location-based services. These range from general application-related ideas to technical aspects. Each chapter starts with a high level of abstraction and drills down to the technical details. Contributors examine each application from all necessary perspectives, namely, requirements, services, data, and scalability. An illustrative example begins early in the book and runs throughout, serving as a reference.· This book defines the LBS field and identifies its capabilities, challenges, and technologies.· The contributors are recognized experts from academia and industry.· Coverage includes navigation systems, middleware, interoperability, standards, and mobile communications.· A sample application, the "find-friend" application, is used throughout the book to integrate the concepts discussed in each chapter.

Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. - Guides managers through developing, administering, or simply understanding business intelligence technology - Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization's data into actionable knowledge - Contains a handy, quick-reference to technologies and terminology